Real-Time Applications of Machine Learning in Cyber-Physical Systems

Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live.The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians. Additional ISBNs 1799893081, 1799893103, 9781799893080, 9781799893103Real-Time Applications of Machine Learning in Cyber-Physical Systems and published by Engineering'science Reference. ISBNs for Real-Time Applications of Machine Learning in Cyber-Physical Systems are 9781799893110, 1799893111 and the print ISBNs are 9781799893080, 1799893081. Additional ISBNs include 1799893081, 1799893103, 9781799893080, 9781799893103.